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Bringing Machine Learning to Software-Defined Networks

Part of the SpringerBriefs in Computer Science Book Series

DOI: 10.1007/978-981-19-4874-9

ISBN: 978-981-19-4873-2

Publisher: Springer Nature Singapore - Springer Journals

Jun 23, 2022

Available

Machine Learning for Software-Defined Networking

Guo, Zehua

Pages: 1 –6

[In this chapter, we introduce software-defined networking, and its two typical application scenarios: wide area networks and data center networks. We also briefly introduce emerging machine learning techniques to improve network performance that are used in the rest of this book.]
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Deep Reinforcement Learning-Based Traffic Engineering in SD-WANs

Guo, Zehua

Pages: 7 –22

[In this chapter, we introduce ScaleDRL, which combines the control theory and DRL to achieve an efficient network control scheme for Traffic Engineering (TE). ScaleDRL employs the pinning control to select a subset of links in the network as critical links and uses a DRL algorithm to dynamically...
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Multi-Agent Reinforcement Learning-Based Controller Load Balancing in SD-WANs

Guo, Zehua

Pages: 23 –38

[In this chapter, we introduce a dynamic controller workload balancing scheme named MARVEL using emerging MARL for switch migration. We design a DRL framework for each agent in the MARL model. The DRL-based solution takes the workload pattern in the control plane as input and generates the...
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Deep Reinforcement Learning-Based Flow Scheduling for Power-Efficient Data Center Networks

Guo, Zehua

Pages: 39 –52

[In this chapter, we introduce SmartFCT, which employs DRL coupled with SDN to improve the power efficiency of DCNs and guarantee FCT. SmartFCT dynamically collects traffic distribution from switches to train its DRL model. The well-trained DRL agent of SmartFCT can quickly analyze the...
Available

Graph Neural Network-Based Coflow Scheduling in Data Center Networks

Guo, Zehua

Pages: 53 –65

[In this chapter, we introduce DeepWeave, a DRL framework to generate coflow scheduling policies. DeepWeave works for both the intra-coflow scheduling and inter-coflow scheduling. To improve the inter-coflow scheduling ability in the job, DeepWeave employs a GNN to process directed-acyclic graph...
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Conclusion and Future Work

Guo, Zehua

Pages: 67 –68

[In this book, we introduce two critical application scenarios for SDN: SD-WANs and SD-DCNs. To improve network performance of SD-WANs and SD-DCNs, we leverage emerging ML techniques (i.e., DRL, MARL, and GNN) to maintain the load balance, increase the power efficiency, and improve the QoS. This...

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